Feature selection in pathological voice classification using dinamyc of component analysis

This paper presents a methodology for the reduction of the training space based on the analysis of the variation of the linear components of the acoustic features. The methodology is applied to the automatic detection of voice disorders by means of stochastic dynamic models. The acoustic features used to model the speech are: MFCC, HNR, GNE, NNE and the energy envelopes. The feature extraction is carried out by means of PCA, and classification is done using discrete and continuous HMMs. The results showed a direct relationship between the principal directions (feature weights) and the classification performance. The dynamic feature analysis by means of PCA reduces the dimension of the original feature space while the topological complexity of the dynamic classifier remains unchanged. The experiments were tested with Kay Elemetrics (DB1) and UPM (DB2) databases. Results showed 91% of accuracy with 30% of computational cost reduction for DB1

Automated detection of voice disorder in the Saarbrücken voice database: Effects of pathology subset and audio materials

The Saarbrücken Voice Database contains speech and simultaneous electroglottography recordings of 1002 speakers exhibiting a wide range of voice disorders, together with recordings of 851 controls. Previous studies have used this database to build systems for automated detection of voice disorders and for differential diagnosis. These studies have varied considerably in the subset of pathologies tested, the audio materials analyzed, the cross-validation method used and the performance metric reported. This variation has made it hard to determine the most promising approaches to the problem of detecting voice disorders. In this study we reimplement three recently published systems that have been trained to detect pathology using the SVD and compare their performance on the same pathologies with the same audio materials using a common cross-validation protocol and performance metric. We show that under this approach, there is much less difference in performance across systems than in their original publication. We also show that voice disorder detection on the basis of a short phrase gives similar performance to that based on a sequence of vowels of different pitch. Our evaluation protocol may be useful for future studies on voice disorder detection with the SVD

Automated Voice Pathology Discrimination from Continuous Speech Benefits from Analysis by Phonetic Context

In contrast to previous studies that look only at discriminating pathological voice from the normal voice, in this study we focus on the discrimination between cases of spasmodic dysphonia (SD) and vocal fold palsy (VP) using automated analysis of speech recordings. The hypothesis is that discrimination will be enhanced by studying continuous speech, since the different pathologies are likely to have different effects in different phonetic contexts. We collected audio recordings of isolated vowels and of a read passage from 60 patients diagnosed with SD (N=38) or VP (N=22). Baseline classifiers on features extracted from the recordings taken as a whole gave a cross-validated unweighted average recall of up to 75% for discriminating the two pathologies. We used an automated method to divide the read passage into phone-labelled regions and built classifiers for each phone. Results show that the discriminability of the pathologies varied with phonetic context as predicted. Since different phone contexts provide different information about the pathologies, classification is improved by fusing phone predictions, to achieve a classification accuracy of 83%. The work has implications for the differential diagnosis of voice pathologies and contributes to a better understanding of their impact on speech

Effects of audio compression in automatic detection of voice pathologies

This paper investigates the performance of an automatic system for voice pathology detection when the voice samples have been compressed in MP3 format and different binary rates (160, 96, 64, 48, 24, and 8 kb/s). The detectors employ cepstral and noise measurements, along with their derivatives, to characterize the voice signals. The classification is performed using Gaussian mixtures models and support vector machines. The results between the different proposed detectors are compared by means of detector error tradeoff (DET) and receiver operating characteristic (ROC) curves, concluding that there are no significant differences in the performance of the detector when the binary rates of the compressed data are above 64 kb/s. This has useful applications in telemedicine, reducing the storage space of voice recordings or transmitting them over narrow-band communications channels